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Assessing recall bias and measurement error in high-frequency social data collection for human-environment research

A major impediment to understanding human-environment interactions is that data on social systems are not collected in a way that is easily comparable to natural systems data. While many environmental variables are collected with high frequency, gridded in time and space, social data is typically co...

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Autores principales: Bell, Andrew, Ward, Patrick, Tamal, Md. Ehsanul Haque, Killilea, Mary
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Nature B.V. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7745111/
https://www.ncbi.nlm.nih.gov/pubmed/33487786
http://dx.doi.org/10.1007/s11111-019-0314-1
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author Bell, Andrew
Ward, Patrick
Tamal, Md. Ehsanul Haque
Killilea, Mary
author_facet Bell, Andrew
Ward, Patrick
Tamal, Md. Ehsanul Haque
Killilea, Mary
author_sort Bell, Andrew
collection PubMed
description A major impediment to understanding human-environment interactions is that data on social systems are not collected in a way that is easily comparable to natural systems data. While many environmental variables are collected with high frequency, gridded in time and space, social data is typically conducted irregularly, in waves that are far apart in time. These efforts typically engage respondents for hours at a time, and suffer from decay in participants’ ability to recall their experiences over long periods of time. Systematic use of mobile and smartphones has the potential to transcend these challenges, with a critical first step being an evaluation of where survey respondents experience the greatest recall decay. We present results from, to our knowledge, the first systematic evaluation of recall bias in components of a household survey, using the Open Data Kit (ODK) platform on Android smartphones. We tasked approximately 500 farmers in rural Bangladesh with responding regularly to components of a large household survey, randomizing the frequency of each task to be received weekly, monthly, or seasonally. We find respondents’ recall of consumption and experience (such as sick days) to suffer much more greatly than their recall of the use of their households’ time for labor and farm activities. Further, we demonstrate a feasible and cost-effective means of engaging respondents in rural areas to create and maintain a true socio-economic “baseline” to mirror similar efforts in the natural sciences.
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spelling pubmed-77451112021-01-21 Assessing recall bias and measurement error in high-frequency social data collection for human-environment research Bell, Andrew Ward, Patrick Tamal, Md. Ehsanul Haque Killilea, Mary Popul Environ Original Paper A major impediment to understanding human-environment interactions is that data on social systems are not collected in a way that is easily comparable to natural systems data. While many environmental variables are collected with high frequency, gridded in time and space, social data is typically conducted irregularly, in waves that are far apart in time. These efforts typically engage respondents for hours at a time, and suffer from decay in participants’ ability to recall their experiences over long periods of time. Systematic use of mobile and smartphones has the potential to transcend these challenges, with a critical first step being an evaluation of where survey respondents experience the greatest recall decay. We present results from, to our knowledge, the first systematic evaluation of recall bias in components of a household survey, using the Open Data Kit (ODK) platform on Android smartphones. We tasked approximately 500 farmers in rural Bangladesh with responding regularly to components of a large household survey, randomizing the frequency of each task to be received weekly, monthly, or seasonally. We find respondents’ recall of consumption and experience (such as sick days) to suffer much more greatly than their recall of the use of their households’ time for labor and farm activities. Further, we demonstrate a feasible and cost-effective means of engaging respondents in rural areas to create and maintain a true socio-economic “baseline” to mirror similar efforts in the natural sciences. Springer Nature B.V. 2019-02-07 2019 /pmc/articles/PMC7745111/ /pubmed/33487786 http://dx.doi.org/10.1007/s11111-019-0314-1 Text en © The Author(s) 2019 http://creativecommons.org/licenses/by/4.0/ This article is distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Original Paper
Bell, Andrew
Ward, Patrick
Tamal, Md. Ehsanul Haque
Killilea, Mary
Assessing recall bias and measurement error in high-frequency social data collection for human-environment research
title Assessing recall bias and measurement error in high-frequency social data collection for human-environment research
title_full Assessing recall bias and measurement error in high-frequency social data collection for human-environment research
title_fullStr Assessing recall bias and measurement error in high-frequency social data collection for human-environment research
title_full_unstemmed Assessing recall bias and measurement error in high-frequency social data collection for human-environment research
title_short Assessing recall bias and measurement error in high-frequency social data collection for human-environment research
title_sort assessing recall bias and measurement error in high-frequency social data collection for human-environment research
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7745111/
https://www.ncbi.nlm.nih.gov/pubmed/33487786
http://dx.doi.org/10.1007/s11111-019-0314-1
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